During the course, you’ll learn everything needed to participate in real competitions — that’s the main goal. Along the way you’ll also gain useful skills for which competitive programmers are so highly valued by employers: ability to write efficient, reliable, and compact code, manage your time well when it’s limited, apply basic algorithmic ideas to real problems, etc.
We start from the very beginning by teaching you what competitions there are, what are their rules, what specifics problems have, how to read problem statements, how to organize your work, and what you should and shouldn’t do. So it’s fine if you’ve never taken part in programming competitions before.
We’ll focus on skills essential to competitive programming: inventing solutions and proving their correctness, estimating their running time, testing and debugging programs, how to benefit from structuring code. We’ll also cover basic algorithmic ideas: brute force search, dynamic programming, greedy algorithms, segment trees.
On competitions, there are a lot of specific pitfalls, perilous to beginners — but that’s not to worry, as we’ll go through the most common of them: integer overflow and issues with fractional numbers, troubles of particular programming languages, how to get unstuck in general.
And, you’ll hone all these skills by solving practice problems, which are just like problems on real competitions. You could use any of the following programming languages: C, C++, C#, Haskell, Java, JavaScript, Python 2, Python 3, Ruby, Scala. We assume that you already know how to write simplest programs in one of these.

从本节课中

CORRECTNESS FIRST

In this module, we'll start with the most basic things you need to actually solve algorithmic problems. First, we'll talk about structuring your code and intuition behind it — why it's very important, how to manage dependencies between parts of different purpose, how intuitive rules are enforced through formal invariants and conditions. We'll also identify a special class of solutions — brute force solutions — which are always correct, but often very slow. And we'll learn how to estimate running time of our solutions by using a powerful concept of big-O notation.